• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.16 no.11
• /
• pp.3523-3543
• /
• 2022

Autonomous vehicles are gradually being regarded as the mainstream trend of future development of the automobile industry. Autonomous driving networks generate many intensive and delay-sensitive computing tasks. The storage space, computing power, and battery capacity of autonomous vehicle terminals cannot meet the resource requirements of the tasks. In this paper, we focus on the task scheduling problem of autonomous driving in software-defined edge networks. By analyzing the intensive and delay-sensitive computing tasks of autonomous vehicles, we propose an emotion model that is related to task urgency and changes with execution time and propose an optimal base station (BS) task scheduling (OBSTS) algorithm. Task sentiment is an important factor that changes with the length of time that computing tasks with different urgency levels remain in the queue. The algorithm uses task sentiment as a performance indicator to measure task scheduling. Experimental results show that the OBSTS algorithm can more effectively meet the intensive and delay-sensitive requirements of vehicle terminals for network resources and improve user service experience.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2021.10a
• /
• pp.689-692
• /
• 2021

This paper is a prior study to improve the efficiency of offloading based on mobile agents to optimize allocation of computing resources and reduce latency that support user proximity of application services in a Fog/Edge Computing (FEC) environment. We propose an algorithm that effectively reduces the execution time and the amount of memory required when extracting optimal moving patterns from the vast set of spatio-temporal movement history data of moving objects. The proposed algorithm can be useful for the distribution and deployment of computing resources for computation offloading in future FEC environments through frequency-based optimal path extraction.

• Electronics and Telecommunications Trends
• /
• v.33 no.6
• /
• pp.24-33
• /
• 2018

In the age of IoT, in which everything is connected to a network, there have been increases in the amount of data traffic, latency, and the risk of personal privacy breaches that conventional cloud computing technology cannot cope with. The idea of edge computing has emerged as a solution to these issues, and furthermore, the concept of ultra-low power edge intelligent semiconductors in which the IoT device itself performs intelligent decisions and processes data has been established. The key elements of this function are an intelligent semiconductor based on artificial intelligence, connectivity for the efficient connection of neurons and synapses, and a large-scale spiking neural network simulation framework for the performance prediction of a neural network. This paper covers the current trends in ultra-low power edge intelligent semiconductors including issues regarding their technology and application.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2022.05a
• /
• pp.114-116
• /
• 2022

The advent of the Multi-access Edge Computing (MEC) paradigm allows mobile users to offload resource-intensive and delay-stringent services to nearby servers, thereby significantly enhancing the quality of experience. Due to erratic roaming of mobile users in the network environment, maintaining maximum quality of experience becomes challenging as they move farther away from the serving edge server, particularly due to the increased latency resulting from the extended distance. The services could be migrated, under policies obtained using Deep Reinforcement Learning (DRL) techniques, to an optimal edge server, however, this operation incurs significant costs in terms of service downtime, thereby adversely affecting service quality of experience. Thus, this study addresses the service mobility problem of deciding whether to migrate and where to migrate the service instance for maximized migration benefits and marginal service downtime.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.14 no.10
• /
• pp.4025-4041
• /
• 2020

Mobile edge computing (MEC) is capable of providing services to smart devices nearby through radio access networks and thus improving service experience of users. In this paper, an offloading strategy for the joint optimization of computing and communication resources in multi-user and multi-MEC overlapping scene was proposed. In addition, under the condition that wireless transmission resources and MEC computing resources were limited and task completion delay was within the maximum tolerance time, the optimization problem of minimizing energy consumption of all users was created, which was then further divided into two subproblems, i.e. offloading strategy and resource allocation. These two subproblems were then solved by the game theory and Lagrangian function to obtain the optimal task offloading strategy and resource allocation plan, and the Nash equilibrium of user offloading strategy games and convex optimization of resource allocation were proved. The simulation results showed that the proposed algorithm could effectively reduce the energy consumption of users.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2022.07a
• /
• pp.637-640
• /
• 2022

최근 빅데이터 및 인공지능의 중요성이 커짐에 따라 클라우드 시스템을 효율적으로 설계하고 관리하기 위한 연구가 활발히 진행 중이다. 본 논문은 기술 발전으로 각 개인은 고성능의 컴퓨팅 자원을 소유하고 있지만, 이 자원이 대부분 잉여 자원으로써 낭비되고 있다는 점을 착안하여, 잉여 컴퓨팅 자원을 효율적으로 활용하기 위해 엣지 클라우드 환경에서 분산된 자원의 가용성을 확보하기 위한 방법을 제안한다.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.33 no.3
• /
• pp.549-559
• /
• 2023

As mobile edge computing (MEC) is gaining attention as a core technology of 5G networks, edge AI technology of 5G network environment based on mobile user data is recently being used in various fields. However, as in traditional AI security, there is a possibility of adversarial interference of standard 5G network functions within the core network responsible for edge AI core functions. In addition, research on data poisoning attacks that can occur in the MEC environment of standalone mode defined in 5G standards by 3GPP is currently insufficient compared to existing LTE networks. In this study, we explore the threat model for the MEC environment using NWDAF, a network function that is responsible for the core function of edge AI in 5G, and propose a feature selection method to improve the performance of detecting data poisoning attacks for Leaf NWDAF as some proof of concept. Through the proposed methodology, we achieved a maximum detection rate of 94.9% for Slowloris attack-based data poisoning attacks in NWDAF.

• International Journal of Computer Science & Network Security
• /
• v.21 no.6
• /
• pp.107-118
• /
• 2021

The deployment of 5G is in full swing, with a significant yearly growth in the data traffic expected to reach 26% by the year and data consumption to reach 122 EB per month by 2022 [10]. In parallel, the idea of smart cities has been implemented by various governments and private organizations. One of the main objectives of 5G deployment is to help develop and realize smart cities. 5G can support the enhanced data delivery requirements and the mass connection requirements of a smart city environment. However, for specific high-demanding applications like tactile Internet, transportation, and augmented reality, the cloud-based 5G infrastructure cannot deliver the required quality of services. We suggest using multi-access edge computing (MEC) technology for smart cities' environments to provide the necessary support. In cloud computing, the dependency on a central server for computation and storage adds extra cost in terms of higher latency. We present a few scenarios to demonstrate how the MEC, with its distributed architecture and closer proximity to the end nodes can significantly improve the quality of services by reducing the latency. This paper has surveyed the existing work in MEC for 5G and highlights various challenges and opportunities. Moreover, we propose a unique framework based on the use of MEC for 5G in a smart city environment. This framework works at multiple levels, where each level has its own defined functionalities. The proposed framework uses the MEC and introduces edge-sub levels to keep the computing infrastructure much closer to the end nodes.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2016.05a
• /
• pp.348-351
• /
• 2016

Recently on IoT environment, there is necessary of protected network, which is only specific user can access it. Applying OAuth protocol on IoT, it can be easier to construct network authentication system, but it is hard to construct protected network authentication system. And there is weakness of OAuth protocol, which is easily attacked by sniffing Token by attacker. So, it is necessary to secondary authentication for OAuth. In ultimate IoT, the fog computing is essential. Fog computing is extension of cloud that enables networking not only in core system but also in edge system and communication node to node. Strength of fog computing is location awareness, support for mobility, and so on. If authentication in fog computing uses this strength, it can be more specialized in Fog Computing. So, in secondary Authentication, using Cyber-Physical-Social System will increase convenience of user than using existing authentication system, such as authentication certificate, id/password and group key, which is inconvenient for user. This study is about authentication based Cyber-Physical-Social System.

• Journal of the Computational Structural Engineering Institute of Korea
• /
• v.32 no.6
• /
• pp.375-381
• /
• 2019

Structural health monitoring to determine structural conditions at an early stage and to efficiently manage the energy requirements of buildings using systems that collects relevant data, is under active investigation. Structural monitoring requires cutting-edge technology in which construction, sensing, and ICT technologies are combined. However, the scope of application is limited because expensive sensors and specialized technical skills are often required. In this study, a Raspberry Pi module, one of the most widely used single board computers, a Lora module that is capable of long-distance communication at low power, and a high-performance accelerometer are used to construct a wireless edge computing system that can monitor building response over an extended time period. In addition, the Raspberry Pi module utilizes an edge computing algorithm, and only meaningful data is obtained from the vast amount of acceleration data acquired in real-time. The raw data acquired using Wi-Fi communication are compared to the Laura data to evaluate the accuracy of the data obtained using the system.